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CN115882916A - Beam determination method, node and storage medium - Google Patents

Beam determination method, node and storage medium Download PDF

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Publication number
CN115882916A
CN115882916A CN202111399935.8A CN202111399935A CN115882916A CN 115882916 A CN115882916 A CN 115882916A CN 202111399935 A CN202111399935 A CN 202111399935A CN 115882916 A CN115882916 A CN 115882916A
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type
beams
artificial intelligence
determining
configuration parameters
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肖华华
章嘉懿
鲁照华
吴昊
张淑娟
蒋创新
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ZTE Corp
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ZTE Corp
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Priority to CN202111399935.8A priority Critical patent/CN115882916A/en
Priority to PCT/CN2022/131350 priority patent/WO2023088178A1/en
Priority to KR1020247011376A priority patent/KR20240051291A/en
Priority to JP2024525497A priority patent/JP2024540137A/en
Priority to CA3235706A priority patent/CA3235706A1/en
Priority to EP22894715.6A priority patent/EP4395198A1/en
Publication of CN115882916A publication Critical patent/CN115882916A/en
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    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
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    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686—Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695—Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0868—Hybrid systems, i.e. switching and combining
    • H04B7/088—Hybrid systems, i.e. switching and combining using beam selection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H04B17/328—Reference signal received power [RSRP]; Reference signal received quality [RSRQ]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/06—TPC algorithms
    • H04W52/14—Separate analysis of uplink or downlink
    • H04W52/146—Uplink power control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/247—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters where the output power of a terminal is based on a path parameter sent by another terminal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/38—TPC being performed in particular situations
    • H04W52/42—TPC being performed in particular situations in systems with time, space, frequency or polarisation diversity

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Abstract

The application discloses a beam determination method, a node and a storage medium, wherein the method comprises the following steps: receiving a beam configuration parameter; determining a first type of beam according to the beam configuration parameters. The method can overcome the defect that the beam determination in the prior art needs larger pilot frequency resource overhead or higher cost.

Description

Beam determination method, node and storage medium
Technical Field
The present application relates to the field of wireless communications technologies, and in particular, to a beam determination method, a node, and a storage medium.
Background
High frequency has abundant spectrum resources, which is an effective means for improving the performance of a wireless system, but because the carrier frequency of the high frequency is relatively high and the path loss is large, the transmitting direction needs to be aligned to a user according to the beamforming technology, so that energy transmission information is concentrated to overcome the performance attenuation caused by the excessive path loss. A common method for obtaining the direction of a transmission or reception beam is to perform beam scanning in each direction and select a beam direction with better performance as a beam direction for transmitting information. When beam scanning is performed, each beam corresponds to one reference signal resource, and when the beam is very thin, the beam directions to be scanned are very many, and accordingly, the overhead of the reference signal resource is very large. As shown in fig. 1, if a regular beam is used, for example, a single beam based on Discrete Fourier Transform (DFT), more beams in different directions need to be scanned during beam scanning, and thus, a larger pilot resource overhead is needed. As shown in fig. 2, if an irregular beam resource, for example, a beam including a superposition of a plurality of beam directions, is used, beams in a plurality of directions can be simulated at a time, so that the number of beam scans can be reduced, but the beam orientation, beam width, beam gain, and the like of the irregular beam are uncertain. Currently, although Artificial Intelligence (AI) can reduce the number of reference signal resources for beam scanning to some extent, it also requires too many training samples due to the thinner beam in the training parameter stage, which results in too high cost.
Disclosure of Invention
The embodiments of the present application mainly aim to provide a beam determination method, a node, and a storage medium, so as to overcome the disadvantage that a large pilot resource overhead or a high cost is required for determining a beam in the prior art.
The embodiment of the application provides a beam determination method, which comprises the following steps:
receiving a beam configuration parameter;
and determining the first type of beam according to the beam configuration parameters.
The embodiment of the application provides a beam determination method, which comprises the following steps:
determining a beam configuration parameter;
transmitting a beam configuration parameter;
wherein the beam configuration parameters are used for determining the first type of beam.
An embodiment of the present application provides a beam determination apparatus, including:
a receiving module, configured to receive a beam configuration parameter;
and the processing module is used for determining the first type of beam according to the beam configuration parameters.
An embodiment of the present application provides a beam determination apparatus, including:
a determining module for determining a beam configuration parameter;
a transmitting module, configured to transmit a beam configuration parameter;
wherein the beam configuration parameters are used for determining the first type of beam.
An embodiment of the present application provides a communication node, including: a processor, which when executing a computer program, implements a beam determination method as provided in any of the embodiments of the present application.
An embodiment of the present application provides a readable and writable storage medium, including: the readable and writable storage medium stores a computer program which, when executed by a processor, implements a beam determination method as provided in any embodiment of the present application.
The embodiment of the application provides a beam determining method, a node and a storage medium, wherein the method comprises the following steps: receiving a beam configuration parameter; determining a first type of beam according to the beam configuration parameters. The method can determine the first type of wave beams, and can acquire the values of the parameters of the artificial intelligence network based on the determined first type of wave beams, or scan the wave beams to determine the wave beams for transmission, thereby reducing the times of artificial intelligence network training or the times of wave beam scanning, and further overcoming the defects that the wave beams determined in the prior art need larger pilot frequency resource expenditure or higher cost.
Drawings
Fig. 1 is a schematic diagram of beam scanning based on regular beams in the prior art.
Fig. 2 is a schematic diagram of beam scanning based on irregular beams in the prior art.
Fig. 3 is a flowchart of a beam determination method according to an embodiment of the present application.
Fig. 4 is a flowchart of another beam determination method provided in the embodiment of the present application.
Fig. 5 is a schematic structural diagram of a beam determination apparatus according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of another beam determination apparatus according to an embodiment of the present application.
Fig. 7 is a schematic structural diagram of another beam determination apparatus according to an embodiment of the present application.
Fig. 8 is a schematic structural diagram of another beam determination apparatus according to an embodiment of the present application.
Fig. 9 is a schematic structural diagram of a communication node according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
In addition, in the embodiments of the present application, the words "optionally" or "exemplarily" are used for indicating as examples, illustrations or explanations. Any embodiment or design described herein as "optionally" or "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the words "optionally" or "exemplarily" etc. is intended to present the relevant concepts in a concrete fashion.
In this embodiment, the base station such as a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device with a wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, or a terminal device in a 5G network or a network above 5G in the future may be an evolved Node B (eNB or eNodeB) in Long Term Evolution (LTE), long Term Evolution enhanced (LTE-a), or a 5G base station device represented by a New Radio (NR) air interface, or a base station in a future communication system. Further, the base station may also include various network-side devices such as various macro base stations, micro base stations, home base stations, radio remote units, routers, or primary cells (primary cells) and cooperative cells (secondary cells), which is not limited in this embodiment.
Further, in order to facilitate a clearer understanding of the solutions provided in the embodiments of the present application, related concepts related to the embodiments of the present application are described herein, specifically as follows:
the signaling includes, but is not limited to, radio Resource Control (RRC), media Access Control element (MAC CE).
The information of the beam may include at least one of: angle Of Arrival (AOA), angle Of Departure (AOD), ZOD (Zenith angle Of Departure), ZOA (Zenith angle Of Departure), vector or vector index constructed from at least one Of AOA, AOD, ZOD, ZOA, discrete Fourier Transform (DFT) vector, codeword in codebook, transmit beam, receive beam, transmit beam group, receive beam group, transmit beam index, receive beam index, transmit beam group index, receive beam group index. In some embodiments, a beam may refer to a spatial filter or a spatial receive/transmit parameter, and the spatial filter may be at least one of: DFT vectors, precoding vectors, DFT matrices, precoding matrices, or vectors formed by linear combination of a plurality of DFT vectors, vectors formed by linear combination of a plurality of precoding vectors. The beam combination may be a linear combination or a non-linear combination of a plurality of vectors (including but not limited to DFT vectors, precoding vectors, vectors formed by DFT).
In the embodiment of the present application, the index (index) and the indicator (indicator) are concepts that can be replaced with each other, and the vector can be interchanged.
Based on the above concept, the present application provides a flowchart of a beam determination method, which may be applied to a terminal, as shown in fig. 3, and the method may include, but is not limited to, the following steps:
and S301, receiving beam configuration parameters.
In the embodiment of the present application, the beam configuration parameters may be transmitted by a node communicating with the terminal, for example, the base station transmits the beam configuration parameters to the terminal. Optionally, the beam configuration parameter may be determined by the first artificial intelligence network, or optionally, the beam configuration parameter may also be determined by the base station in a preconfigured manner or calculated according to a channel scenario, and the embodiment of the present application is not limited to these manners for determining the beam configuration parameter.
Illustratively, the beam configuration parameters include at least one of:
beam width, number of beam directions, array element phase, array element power, beam gain, beam index, beam combination and random beam initial index.
Further, the beam width in the beam configuration parameters may be used to determine the wide beam; the beam configuration parameters comprise at least one of the number of beam directions, the phase of the array elements and the power of the array elements, and can be used for determining irregular beams or multi-directional beams; the beam configuration parameters include at least one of beam index, beam width, etc., which can be used to determine random beams; the beam configuration parameters include at least one of beam width, array element power, array element phase, etc., which may be used to determine the non-constant modulus beam.
S302, determining the first type of beam according to the beam configuration parameters.
In an embodiment of the present application, the first type of beam may be used to train an artificial intelligence network parameter, for example, to obtain a value of a second artificial intelligence network parameter, or the first type of beam may be used for beam scanning to determine a beam (for example, a second beam) for transmission. Therefore, the times of training the second artificial intelligence network can be reduced or the times of beam scanning can be reduced based on the determined first type of beam, so that the defect that the beam is determined to need larger pilot frequency resource overhead or higher cost in the prior art can be overcome.
In one example, the first type of beam may include at least one of: wide beam, irregular beam, multi-directional beam, random beam, non-constant-touch beam.
Further, the terminal may determine a second type of beam through the first type of beam and the second artificial intelligence network parameter, and the second type of beam may be used for transmitting signaling or data between the terminal and the base station. For example, the terminal performs artificial intelligence network training by using the first type of beam, obtains a value of a second artificial intelligence network parameter, selects an optimal second type of beam index suitable for the terminal to transmit information according to the determined value of the second artificial intelligence network parameter, and transmits data by using the beam corresponding to the optimal second type of beam index.
Further, the terminal may determine the second type of beam through a second artificial intelligence network parameter, and the parameter of the second artificial intelligence network may be obtained according to the first type of beam training.
Exemplarily, in the embodiment of the present application, assuming that the beams are divided into the first type beams and the second type beams according to the beam width, the first type beams may be divided into wide beams, and correspondingly, the second type beams may be divided into narrow beams. The wide beam may be understood as a beam with a 1/K power beam width greater than a first threshold, the narrow beam may be understood as a beam with a 1/K power beam width less than a second threshold, the first threshold and the second threshold are both numbers greater than 0, the second threshold is less than the first threshold, K is an integer greater than 0, and for example, K takes a value of 2, 5, or 10.
Alternatively, the first type of beams may be divided into irregular beams, and the second type of beams may be divided into regular beams, that is, the beams may be divided into two types according to whether the beams are regular or not. An irregular beam is understood to mean, among other things, that the power radiation of the beam is irregular in shape, for example, having a plurality of distinct peaks. A regular beam is understood to be a regular power radiation shape of the beam, such as a beam with only one peak, e.g. a beam constructed with DFT vectors.
Alternatively, the beams may be further divided into multi-directional beams and unidirectional beams, for example, the first type of beams are divided into multi-directional beams, and the second type of beams are divided into unidirectional beams. Wherein the multi-directional beam may be a beam linearly combined by a plurality of DFT vectors, or a beam constructed by a plurality of DFT linear combinations; the unidirectional beam may be a beam formed of one DFT vector or a beam formed of one DFT.
It should be noted that the DFT vector for constructing the beam may include, but is not limited to, a vector formed by Kronecker product (Kronecker product) by using more than two DFT vectors.
Optionally, in this embodiment, the first type of beams may also be divided into random beams, and the second type of beams may also be divided into non-random beams. The random beam refers to a beam whose beam direction is generated in a random manner, such as the wide beam, the narrow beam, the regular beam, the irregular beam, the multi-directional beam, and the unidirectional beam, and these beams randomly generate a pointing direction each time information is transmitted. The non-random beam refers to a beam whose beam direction is determined according to the channel state information, for example, at least one of a narrow beam, a regular beam, and a unidirectional beam.
In one example, the beams may be further divided into two types, non-galvanostatic beams and galvanostatic beams, e.g., a first type of beams is divided into non-galvanostatic beams and a second type of beams is divided into galvanostatic beams. The non-constant-film beam means that the absolute value of each element in the vector corresponding to the beam is at least one different, and the constant-film beam means that the absolute value of each element in the vector corresponding to the beam is the same, such as a DFT vector.
Further, the transmission may include receiving or transmitting, e.g., transmitting information or receiving information. The information includes, but is not limited to, signaling or data, the signaling includes uplink control information, or downlink control information, or radio resources used for carrying various control information, such as a physical uplink control channel, a physical downlink control channel. The data includes uplink data or downlink data, such as a physical shared downlink channel and a physical uplink shared channel.
In one example, a mapping relationship exists between the first type of beam and the second type of beam. For example, taking the terminal as an example, the terminal may determine the mapping relationship between the first type beams and the second type beams based on at least one of the following manners. For example,
the terminal determines the mapping relation between the first type of wave beams and the second type of wave beams through a third artificial intelligence network;
the terminal determines the mapping relation between the first type of beam and the second type of beam in a predetermined mode;
the terminal determines the mapping relation between the first type of beam and the second type of beam by receiving the configuration signaling.
It should be noted that, the mapping relationship between the first type of beam and the second type of beam may be understood that an association relationship exists between the first type of beam and the second type of beam, and the first type of beam and the second type of beam having the association relationship also have a certain similarity in transmission function, so that the second type of neural network parameter trained by the first type of beam may be generalized to be used for selecting the second type of beam, and then the second type of beam may be used for transmitting information (including signaling or data) after the selected beam is replaced by the second type of beam having the association relationship when the first type of beam is scanned. For example, the terminal performs beam scanning with the first type of beam to determine an index of an optimal first type of beam for transmitting information, determines an optimal second type of beam index corresponding to the optimal first type of beam index based on an association relationship between the first type of beam and the second type of beam, and transmits data with a beam corresponding to the optimal second type of beam index.
Optionally, the two beams having the association relationship may have a characteristic of at least one of:
have the same Quasi co-location (QCL) parameter value;
have the same Spatial Rx Parameter value;
have the same Transmission Configuration Indicator (TCI) value;
have the same value of QCL type D (QCL type D);
there is a certain overlap in spatial coverage, for example, the first type beam covers the coverage area of the second type beam, the transmission direction of the first type beam includes the transmission direction of the second type beam, the indication direction of the first type beam includes the indication direction of the second type beam, and so on.
In one example, the terminal may be further configured to receive a gain difference between the first type beam and the second type beam, and the gain difference may be used to determine the transmission power of the second type beam.
Illustratively, the two types of beams having the correlation have a difference in beam gain, for example, the gain of the wide beam is smaller than that of the narrow beam, the gain of the multi-direction is smaller than that of the unidirectional beam, the gain of the irregular beam is smaller than that of the regular beam, and the gain of the non-constant-film beam is smaller than that of the constant-film beam. After obtaining the gain difference between the first type of beam and the second type of beam having the association relationship, the base station may send the gain difference to the terminal, and the terminal adjusts the power for transmitting information according to the received gain difference, so as to avoid making up for the performance loss caused by the difference between the two. For example, when transmitting data or signals, the transmission power of the second type beam is adjusted, or when receiving signals or data, the reception power of the second type beam is adjusted, or a reception power value for calculating channel quality is adjusted according to a gain difference between the first type beam and the second type beam, for example, the power of a signal portion in a calculated signal-to-noise and interference ratio (SINR) is adjusted, and a reception power value in a received Reference signal power (RSRP) is adjusted.
In an example, before the step S301, an implementation manner provided by the embodiment of the present application further includes:
the terminal transmits the terminal beam capabilities to the base station.
Illustratively, the terminal beam capabilities may include at least one of:
a capability to support a first type of beam, a beam type to support a first type of beam, a first type of beam processing capability.
For example, the terminal reports the terminal beam capability to the base station, and after receiving the terminal reported capability, the base station may determine whether to configure the beam configuration parameters for the terminal based on the terminal beam capability reported by the terminal.
In the embodiment of the present application, the first artificial intelligence network, the second artificial intelligence network, and the third artificial intelligence network may all be referred to as artificial intelligence, and the artificial intelligence may include machine learning, deep learning, reinforcement learning, migration learning, deep reinforcement learning, and the like. Illustratively, in some embodiments, the artificial intelligence may be implemented by a neural network, further, the neural network may include at least an input layer, an output layer, at least one hidden layer, wherein each layer of the neural network may include, but is not limited to, using at least one of a fully connected layer, a dense layer, a convolutional layer, a transposed convolutional layer, a direct layer, an activation function, a normalization layer, a pooling layer, and the like. Optionally, each layer of the neural network may further include a sub-neural network, such as a residual network, a dense network, a cyclic network, and so on.
The following describes the relevant functions of each artificial intelligence network in detail, specifically as follows:
the first artificial intelligence network is mainly used for generating a first type of beam suitable for beam scanning or training the second artificial intelligence network. For example, the number of arrays, the phase information of the arrays, the amplitude information, and the tag information of the received energy or RSRP are input, and N beam configuration parameters of the first type of beam used for beam scanning or beam training are trained.
A second artificial intelligence network for selecting at least one beam from a set of beams for receiving or transmitting information, wherein the information includes but is not limited to signaling or data. For example, there are M transmit beams and N receive beams, where M and N are positive integers, and each transmit beam corresponds to at least one channel state information reference signal (CSI-RS) resource (resource) or a set of CSI-RS resources (CSI-RS resource sets). The method comprises the steps of inputting received CSI-RS resource or Reference Signal Received Power (RSRP) corresponding to the CSI-RS resource to a second artificial intelligence network, mapping at least one input RSRP to an optimal beam index, namely a beam index or beam pair index used for information transmission by the second artificial intelligence network, using a corresponding receiving beam for self information transmission, and feeding back a sending beam index to a base station for information transmission by the base station. Alternatively, the RSRP may be replaced by a signal-to-noise and interference ratio (SINR) corresponding to the CSI-RS resource to select a beam index or a beam pair index. Optionally, the M transmission beams may be a subset of all selectable beams, and the N beams may be a subset of all selectable beams, so that the beams in the entire beam set may be predicted in one subset of the beam set by the second artificial intelligence network, that is, a spatial interpolation effect is performed, so as to reduce resource overhead during beam scanning.
For example, assume that there are 128 transmit beams and 32 receive beams, from which M =16 transmit beams and N =4 receive beams are uniformly selected for beam training. The received RSRP for the 16 transmit beams and 4 receive beams is input into the second artificial intelligence network to predict its optimal beam for the case of 128 transmit beams and 32 receive beams. That is, the second artificial intelligence network is mainly used for selecting the second type of beam, but when the parameters of the second artificial network are trained, the first type of beam can be used to reduce the times or expenses of the trained beams.
And the third artificial intelligence network is mainly used for associating or mapping the first type of beams and the second type of beams. Such as inputting a first type of beam and a corresponding received RSRP or SINR, and outputting a corresponding second type of beam or beam index.
In the embodiment of the application, the number of times of training of the second artificial intelligence network is reduced, so that the aim of reducing resource overhead is fulfilled. The terminal receives the beam configuration parameters sent by the base station and generates a beam for transmitting information according to the beam configuration parameters configured by the base station, for example, in an uplink, a transmission beam for transmitting information is generated. The terminal transmits information on M Sounding reference signal resources (SRS resources) according to the generated M transmission beams, and the base station receives the transmitted reference pilot signals, such as the SRS resources used for beam training, with N reception beams, and uses at least one of reception information corresponding to the M transmission beams received by the N reception beams, or RSRP or reception SINR corresponding to the reception information, as a sample. The optimal transmit beam index and receive beam index corresponding to the sample may be used as tags, and the samples and tags of the plurality of terminals at least one time are collected and used as input of the second artificial intelligence network to update the second artificial intelligence network parameters.
Optionally, in order to reduce the number of times of beam scanning and thus achieve the purpose of reducing resource overhead, the terminal may receive a beam configuration parameter sent by the base station, and generate a beam for transmitting information according to the beam configuration parameter configured by the base station, for example, in an uplink, generate a transmit beam for transmitting information. The terminal sends information on M Sounding reference signal resources (SRS resources) according to the generated M transmit beams, and the base station receives the transmitted reference pilot signals with N receive beams, for example, the SRS resources for beam training, and uses at least one of the receive information corresponding to the M transmit beams received by the N receive beams, or RSRP or receive SINR corresponding to the receive information, as a basis for selecting beams, for example, the transmit beam and the receive beam corresponding to the maximum RSRP or SINR corresponding to the M N transmit beams are used as beams for transmitting information.
Fig. 4 is a flowchart of a beam determination method provided in an embodiment of the present application, where the method may be applied to a base station, as shown in fig. 4, and the method may include, but is not limited to, the following steps:
s401, determining beam configuration parameters.
In this embodiment, the base station may determine the beam configuration parameter through the first artificial intelligence network. For example, input information required for training is input in the first artificial intelligence network, and beam configuration parameters of the first type of beams used for beam scanning or beam training are trained through the first artificial intelligence network.
The beam configuration parameters may include at least one of: beam width, number of beam directions, array element phase, array element power, beam gain, beam index, beam combination and random beam initial index.
Further, the beam width in the beam configuration parameters may be used to determine the wide beam; the beam configuration parameters comprise at least one of the number of beam directions, the phase of the array elements and the power of the array elements, and can be used for determining irregular beams or multidirectional beams; the beam configuration parameters include at least one of beam index, beam width and the like, which can be used for determining random beams; the beam configuration parameters include at least one of beam width, array element power, array element phase, etc. that may be used to determine the non-constant-modulus beam.
And S402, transmitting the beam configuration parameters.
The beam configuration parameter may be used to determine a first type of beam, where the first type of beam may be used to train an artificial intelligence network parameter, for example, to obtain a value of a second artificial intelligence network parameter, or the first type of beam may be used to perform beam scanning to determine a beam (for example, a second beam) used for transmission, so that the number of times of training of the second artificial intelligence network may be reduced based on the determined first type of beam, or the number of times of beam scanning may be reduced, thereby overcoming a drawback that the beam determined in the prior art needs a large pilot resource overhead or a high cost.
Illustratively, the first type of beam may include at least one of:
wide beam, irregular beam, multi-directional beam, random beam, non-constant-touch beam.
Optionally, the first type of beam and the second artificial intelligence network parameter may be used to determine a second type of beam for transmitting information. For example, the terminal performs artificial intelligence network training by using the first type of beam, obtains a value of a second artificial intelligence network parameter, selects an optimal second type of beam index suitable for the terminal to transmit information according to the determined value of the second artificial intelligence network parameter, and transmits the information by using the beam corresponding to the optimal second type of beam index.
Optionally, in this embodiment of the present application, beam division may be performed in the following manner, and the beams are divided into two types, i.e., a first type beam and a second type beam. For example, the beams are divided into beams of a first type and beams of a second type according to the beam width, and then the beams of the first type may be divided into wide beams and the beams of the second type may be divided into narrow beams. The wide beam may be understood as a beam with a 1/K power beam width greater than a first threshold, the narrow beam may be understood as a beam with a 1/K power beam width less than a second threshold, the first threshold and the second threshold are both numbers greater than 0, the second threshold is less than the first threshold, K is an integer greater than 0, and for example, K takes a value of 2, 5, or 10.
Alternatively, the first type of beams may be divided into irregular beams and the second type of beams may be divided into regular beams, that is, the beams may be divided into two types according to whether the beams are regular or not. An irregular beam is understood to mean, among other things, that the power radiation of the beam is irregular in shape, for example, having a plurality of distinct peaks. Regular beams may be understood as power radiation shape rules of the beam, such as a beam with only one peak, e.g. a beam constructed with DFT vectors.
Alternatively, the beams may be further divided into multi-directional beams and unidirectional beams, for example, the first type of beams are divided into multi-directional beams, and the second type of beams are divided into unidirectional beams. Wherein the multi-directional beam may be a beam linearly combined by a plurality of DFT vectors, or a beam constructed by a plurality of DFT linear combinations; the unidirectional beam may be a beam formed by one DFT vector or a beam constructed by one DFT. It should be noted that the DFT vector for constructing the beam may include, but is not limited to, a vector formed by a Kronecker product (Kronecker product) with more than two DFT vectors.
Alternatively, the first type of beam may be divided into random beams and the second type of beam may be divided into non-random beams. Here, the random beam refers to a beam whose beam direction is generated in a random manner, such as the above-mentioned wide beam, narrow beam, regular beam, irregular beam, multi-directional beam, and unidirectional beam, and these beams randomly generate a pointing direction each time information is transmitted. The non-random beam refers to a beam whose beam direction is determined according to the channel state information, for example, at least one of a narrow beam, a regular beam, and a unidirectional beam.
In one example, the beams may be further divided into two types, a non-constant film beam and a constant film beam, for example, the first type of beam is divided into a non-constant film beam and the second type of beam is divided into a constant film beam. The non-constant-film beam means that the absolute value of each element in the vector corresponding to the beam is at least one different, and the constant-film beam means that the absolute value of each element in the vector corresponding to the beam is the same, such as a DFT vector.
Further, the second beam transmission information may include transmission information or reception information. The information includes, but is not limited to, signaling or data, the signaling includes uplink control information, or downlink control information, or radio resources used for carrying various control information, such as a physical uplink control channel, a physical downlink control channel. The data includes uplink data or downlink data, such as a physical shared downlink channel and a physical uplink shared channel.
In one example, a mapping relationship exists between the first type of beam and the second type of beam.
Further, the base station may determine the mapping relationship between the first type of beams and the second type of beams by at least one of the following methods, for example:
determining the mapping relation between the first type of beams and the second type of beams through a third artificial intelligence network;
determining the mapping relation between the first type of beams and the second type of beams in an agreed mode;
and determining the mapping relation between the first type of beams and the second type of beams by sending configuration signaling.
It should be noted that, the mapping relationship between the first type of beam and the second type of beam may be understood that an association relationship exists between the first type of beam and the second type of beam, and the first type of beam and the second type of beam having the association relationship also have a certain similarity in transmission function, so that the second type of neural network parameter trained by the first type of beam may be generalized to be used for selecting the second type of beam, and then the second type of beam may be used for transmitting information (including signaling or data) after the selected beam is replaced by the second type of beam having the association relationship when the first type of beam is scanned. For example, the terminal performs beam scanning with the first type of beam to determine an index of an optimal first type of beam for transmitting information, determines an optimal second type of beam index corresponding to the optimal first type of beam index based on an association relationship between the first type of beam and the second type of beam, and transmits data with a beam corresponding to the optimal second type of beam index.
Optionally, the two beams having the association relationship may have a characteristic of at least one of:
have the same Quasi co-location (QCL) parameter value;
have the same Spatial Rx Parameter value;
have the same Transmission Configuration Indicator (TCI) value;
have the same QCL type D (QCL type D) value;
there is a certain overlap in spatial coverage, for example, the first type beam covers the coverage area of the second type beam, the transmission direction of the first type beam includes the transmission direction of the second type beam, the indication direction of the first type beam includes the indication direction of the second type beam, and so on.
Optionally, the base station may further send, to the terminal, a gain difference between the first type beam and the second type beam, where the gain difference is used to determine the transmission power of the second type beam.
Since the two types of beams having the correlation have a difference in beam gain, for example, the gain of the wide beam is smaller than that of the narrow beam, the gain of the multi-direction is smaller than that of the unidirectional beam, the gain of the irregular beam is smaller than that of the regular beam, and the gain of the non-constant-film beam is smaller than that of the constant-film beam. Then, after obtaining the gain difference between the first type beam and the second type beam having the association relationship, the base station may send the gain difference to the terminal, and the terminal adjusts the power for transmitting information according to the received gain difference, so as to avoid making up for the performance loss caused by the difference between the two.
Optionally, before the step S401, the base station may further receive a terminal beam capability reported by the terminal, where the terminal beam capability may include at least one of the following:
a capability to support a first type of beam, a beam type to support a first type of beam, a first type of beam processing capability.
The terminal reports the terminal beam capacity to the base station, and after receiving the terminal reported capacity, the base station can determine whether to configure beam configuration parameters for the terminal based on the terminal reported capacity.
In the embodiment of the application, the first artificial intelligence network is mainly used for generating the first type of beams suitable for beam scanning or training the second artificial intelligence network. For example, the number of the input arrays, the phase information of the arrays, the amplitude information, and the tag information of the received energy or RSRP are input to train N beam configuration parameters of the first type of beam for beam scanning or beam training.
A second artificial intelligence network for selecting at least one beam from a set of beams for receiving or transmitting information, wherein the information includes but is not limited to signaling or data. For example, there are M transmit beams and N receive beams, where M and N are positive integers, and each transmit beam corresponds to at least one channel state information reference signal (CSI-RS) resource (resource) or a CSI-RS resource set (CSI-RS resource set). Inputting the received CSI-RS resource or Reference Signal Received Power (RSRP) corresponding to the CSI-RS resource to a second artificial intelligence network, mapping at least one input RSRP to an optimal beam index, namely a beam index or a beam pair index for information transmission, using the corresponding receiving beam for transmitting information, and feeding back the sending beam index to a base station for transmitting the information. Alternatively, the RSRP may be replaced by a signal-to-noise and interference ratio (SINR) corresponding to the CSI-RS resource to select a beam index or a beam pair index. Optionally, the M transmission beams may be a subset of all selectable beams, and the N beams may be a subset of all selectable beams, so that the beams in the entire beam set may be predicted in one subset of the beam set by the second artificial intelligence network, that is, a spatial interpolation effect is performed, so as to reduce resource overhead during beam scanning.
For example, assuming that there are 128 transmit beams and 32 receive beams, M =16 transmit beams and N =4 receive beams are uniformly selected therefrom for beam training. The received RSRP for the 16 transmit beams and 4 receive beams is input into the second artificial intelligence network to predict its optimal beam for the case of 128 transmit beams and 32 receive beams. That is, the second artificial intelligence network is mainly used for selecting the second type of beams, but when the parameters of the second artificial network are trained, the first type of beams can be used to reduce the times or expenses of the trained beams.
And the third artificial intelligence network is mainly used for associating or mapping the first type of beams and the second type of beams. Such as inputting a first type of beam and a corresponding received RSRP or SINR, and outputting a corresponding second type of beam or beam index.
For the base station side, the number of times of training of the second artificial intelligence network is reduced, so that the aim of reducing resource overhead is fulfilled. The base station may configure a beam configuration parameter for the terminal based on the acquired terminal beam capability, and send the configured beam configuration parameter to the terminal. After receiving the beam configuration parameters, the terminal may generate a beam for transmitting information based on the beam configuration parameters. Such as in the downlink, receive beams are generated for receiving information. The terminal receives, according to the generated M reception beams, reference pilot signals, such as CSI-RS resources used for beam training, which are transmitted by the base station on the N transmission beams, respectively, and takes at least one of reception information corresponding to the N reception beams received by the M reception beams, or RSRP or reception SINR corresponding to the reception information, as one sample. And the optimal transmitting beam index and receiving beam index corresponding to the sample are used as labels, and the samples and labels of the plurality of terminals at least one moment are collected and used as the input of the second artificial intelligent network so as to update the parameters of the second artificial intelligent network.
Optionally, the number of beam scans is reduced, so as to reduce resource overhead. The base station may configure a beam configuration parameter for the terminal based on the acquired terminal beam capability, and send the configured beam configuration parameter to the terminal. The terminal receives the beam configuration parameters sent by the base station, and determines a beam for transmitting information according to the received beam configuration parameters, for example, in a downlink, a receiving beam for receiving information is generated. The terminal receives, according to the generated M receiving beams, reference pilot signals, such as CSI-RS resources used for beam training, respectively transmitted by the base station on the N transmitting beams, and uses at least one of receiving information corresponding to the N receiving beams respectively received by the M receiving beams, or RSRP or receiving SINR corresponding to the receiving information, as a basis for selecting beams, for example, the transmitting beam and the receiving beam corresponding to the maximum RSRP or SINR corresponding to the M × N receiving beams are used as beams for transmitting information.
Fig. 5 is a schematic structural diagram of a beam determining apparatus according to an embodiment of the present application, and as shown in fig. 5, the apparatus may include: a receiving module 501 and a processing module 502;
the receiving module is used for receiving the beam configuration parameters;
and the processing module is used for determining the first type of beam according to the beam configuration parameters.
Alternatively, the beam configuration parameters may be determined by the first artificial intelligence network.
Illustratively, the beam configuration parameters include at least one of:
the beam width, the number of beam directions, the phase of array elements, the power of array elements, the beam gain, the beam index, the beam combination and the initial index of random beams.
The first type of beam comprises at least one of:
wide beam, irregular beam, multidirectional beam, random beam, non-constant-touch beam.
Further, the first type of beam is used for obtaining a value of a second artificial intelligence network parameter.
In an example, the processing module is further configured to determine a second type of beam according to the first type of beam and a second artificial intelligence network parameter;
the second type of beam is used for transmitting signaling or data.
Optionally, there is a mapping relationship between the first type of beam and the second type of beam.
In one example, the processing module may be configured to determine, by a third artificial intelligence network, a mapping relationship between the first type of beam and the second type of beam;
the processing module can also determine the mapping relation between the first type of beam and the second type of beam in a convention mode;
the receiving module may be further configured to receive a configuration signaling, and accordingly, the processing module may determine the mapping relationship between the first type of beam and the second type of beam in a manner of the configuration signaling.
In one example, the receiving module may further receive a gain difference between the first type of beam and the second type of beam, the gain difference being used to determine the transmission power of the second type of beam.
As shown in fig. 6, the apparatus may further include a sending module 503;
a sending module, configured to send a terminal beam capability of the device itself, where the terminal beam capability includes at least one of:
a capability to support a first type of beam, a beam type to support a first type of beam, a first type of beam processing capability.
The beam determining apparatus provided in this embodiment is used to implement the beam determining method in the embodiment shown in fig. 3, and the implementation principle and technical effect are similar, which are not described herein again.
Fig. 7 is a schematic structural diagram of a beam determining apparatus according to an embodiment of the present application, and as shown in fig. 7, the apparatus may include: a determining module 701 and a sending module 702;
a determining module for determining a beam configuration parameter;
a transmitting module, configured to transmit a beam configuration parameter;
wherein the beam configuration parameters are used for determining the first type of beam.
Alternatively, the beam configuration may be determined by the first artificial intelligence network.
Illustratively, the beam configuration parameters include at least one of:
the beam width, the number of beam directions, the phase of array elements, the power of array elements, the beam gain, the beam index, the beam combination and the initial index of random beams.
The first type of beam comprises at least one of:
wide beam, irregular beam, multidirectional beam, random beam, non-constant-touch beam.
Furthermore, the first type of beam is also used for obtaining the value of the second artificial intelligence network parameter.
In one example, the first type of beam and the second artificial intelligence network parameters described above may be used to determine a second type of beam that is used to transmit signaling or data.
Optionally, there is a mapping relationship between the first type of beam and the second type of beam.
As shown in fig. 8, in an example, the apparatus may further include a processing module 703 and a receiving module 704;
the processing module is used for determining the mapping relation between the first type of beams and the second type of beams through a third artificial intelligence network;
the processing module can also determine the mapping relation between the first type of beams and the second type of beams in an agreed manner;
optionally, the sending module may further send a configuration signaling, and accordingly, the processing module may further determine the mapping relationship between the first type beam and the second type beam through the configuration signaling.
In an example, the transmitting module may be further configured to transmit a gain difference between the first type of beam and the second type of beam, and the gain difference is used to determine the transmission power of the second type of beam.
A receiving module operable to receive a terminal beam capability, the terminal beam capability comprising at least one of:
a capability to support a first type of beam, a beam type to support a first type of beam, a first type of beam processing capability.
The beam determining apparatus provided in this embodiment is used to implement the beam determining method in the embodiment shown in fig. 4, and the implementation principle and the technical effect are similar, which are not described herein again.
Fig. 9 is a schematic structural diagram of a communication node according to an embodiment, as shown in fig. 9, the node includes a processor 901 and a memory 902; the number of processors 901 in a node may be one or more, and one processor 901 is taken as an example in fig. 9; the processor 901 and the memory 902 in the node may be connected by a bus or other means, and fig. 9 illustrates the connection by a bus as an example.
The memory 902 may be used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the beam determination methods in the embodiments of fig. 3 and 4 (for example, modules in the beam determination apparatuses provided in the embodiments of fig. 5 to 8). The processor 901 implements the beam determination method described above by running software programs, instructions, and modules stored in the memory 902.
The memory 902 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the set-top box, and the like. Further, the memory 902 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device.
In an example, the processor in the node may also implement the beam determination method through a logic circuit, a gate circuit, and other hardware circuits inside the processor, where possible.
The present embodiments also provide a readable and writable storage medium for computer storage, where one or more programs are stored, and the one or more programs are executable by one or more processors to perform the beam determination method in the foregoing embodiments.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods disclosed above, functional modules/units in the devices, may be implemented as software, firmware, hardware, and suitable combinations thereof.
In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The foregoing description of the exemplary embodiments of the present application with reference to the accompanying drawings is merely illustrative and not intended to limit the scope of the application. Any modifications, equivalents and improvements which may occur to those skilled in the art without departing from the scope and spirit of the present application are intended to be within the scope of the claims of the present application.

Claims (22)

1. A method for beam determination, comprising:
receiving a beam configuration parameter;
and determining the first type of beam according to the beam configuration parameters.
2. The method of claim 1, wherein the beam configuration parameters are determined by a first artificial intelligence network.
3. The method according to claim 1 or 2, wherein the beam configuration parameters comprise at least one of:
the beam width, the number of beam directions, the phase of array elements, the power of array elements, the beam gain, the beam index, the beam combination and the initial index of random beams.
4. The method according to claim 1 or 2, wherein the first type of beam comprises at least one of:
wide beam, irregular beam, multi-directional beam, random beam, non-constant-touch beam.
5. The method of claim 1, wherein the first type of beam is used to obtain values of a second artificial intelligence network parameter.
6. The method of claim 5, further comprising:
determining a second type of wave beam according to the first type of wave beam and the second artificial intelligence network parameter;
the second type of beam is used for transmitting signaling or data.
7. The method of claim 6, wherein the first type of beam and the second type of beam have a mapping relationship.
8. The method of claim 7, further comprising at least one of:
determining a mapping relation between the first type of beams and the second type of beams through a third artificial intelligence network;
determining the mapping relation between the first type of beams and the second type of beams in an agreed manner;
and determining the mapping relation between the first type of beams and the second type of beams by receiving configuration signaling.
9. The method of claim 1 or 5, further comprising:
receiving a gain difference between the first type of beam and the second type of beam;
the gain difference is used to determine the transmission power of the second type of beam.
10. The method of claim 1, wherein prior to the receive beam configuration parameters, the method further comprises:
transmitting a terminal beam capability;
the terminal beam capability comprises at least one of:
a capability to support a first type of beam, a beam type to support a first type of beam, a first type of beam processing capability.
11. A method for beam determination, comprising:
determining a beam configuration parameter;
transmitting the beam configuration parameters;
wherein the beam configuration parameters are used for determining the first type of beam.
12. The method of claim 11, wherein the beam configuration parameters are determined by a first artificial intelligence network.
13. The method according to claim 11 or 12, wherein the beam configuration parameters comprise at least one of:
the beam width, the number of beam directions, the phase of array elements, the power of array elements, the beam gain, the beam index, the beam combination and the initial index of random beams.
14. The method according to claim 11 or 12, wherein the first type of beam comprises at least one of:
wide beam, irregular beam, multi-directional beam, random beam, non-constant-touch beam.
15. The method of claim 11, wherein the first type of beam is used to obtain values of a second artificial intelligence network parameter.
16. The method of claim 15, wherein the first type of beam and the second artificial intelligence network parameters are used to determine a second type of beam;
the second type of beam is used for transmitting signaling or data.
17. The method of claim 16, wherein the first type of beam and the second type of beam have a mapping relationship.
18. The method of claim 17, further comprising at least one of:
determining a mapping relation between the first type of beams and the second type of beams through a third artificial intelligence network;
determining the mapping relation between the first type of beams and the second type of beams in a convention manner;
and determining the mapping relation between the first type of beams and the second type of beams by sending configuration signaling.
19. The method according to claim 11 or 15, further comprising:
transmitting a gain difference between the first type of beam and the second type of beam;
the gain difference is used to determine the transmission power of the second type of beam.
20. The method of claim 11, wherein prior to the transmitting the beam configuration parameters, the method further comprises:
receiving a terminal beam capability;
the terminal beam capability comprises at least one of:
a capability to support a first type of beam, a beam type to support a first type of beam, a first type of beam processing capability.
21. A communications node, comprising: a processor which, when executing a computer program, implements the beam determination method of any of claims 1-10 or the beam determination method of any of claims 11-20.
22. A readable and writable storage medium, characterized in that the readable and writable storage medium stores a computer program which, when executed by a processor, implements the beam determination method according to any one of claims 1-10 or the beam determination method according to any one of claims 11-20.
CN202111399935.8A 2021-11-19 2021-11-19 Beam determination method, node and storage medium Pending CN115882916A (en)

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